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Datasets:
SpeechAntiSpoofingBenchmarks
/
PyAra

Tasks:
Audio Classification
Modalities:
Audio
Text
Formats:
parquet
Languages:
Russian
Size:
100K - 1M
Tags:
anti-spoofing
audio-deepfake-detection
speech
benchmark
arena-ready
russian
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
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33
PyAra
File size: 2,733 Bytes
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schema_version: 4
system:
  name: Nes2Net
  slug: nes2net
  description: wav2vec 2.0 (XLS-R 300M) self-supervised front-end fine-tuned end-to-end with a Nes2Net-X
    (Nested Res2Net TDNN) back-end for speech anti-spoofing. The nested Res2Net structure couples multi-scale
    residual groups with squeeze- excitation, replacing dimensionality-reducing necks; mean temporal pooling
    + linear classifier. Only ~0.51M back-end params. Official Nes2Net-X single checkpoint (ASVspoof2021
    LA 1.73% / DF 1.65% EER as reported), trained on ASVspoof2019 LA with RawBoost, FP32, deterministic
    first-64600-sample window (no random crop).
  code: https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW
  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Nes2Net
  params_millions: 317.9026
  paper:
    arxiv_id: '2504.05657'
    url: https://arxiv.org/abs/2504.05657
    bibtex: "@article{Nes2Net,\n  author={Liu, Tianchi and Truong, Duc-Tuan and Das, Rohan Kumar and Lee,\
      \ Kong Aik and Li, Haizhou},\n  journal={IEEE Transactions on Information Forensics and Security},\n\
      \  title={Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-Spoofing},\n\
      \  year={2025},\n  volume={20},\n  pages={12005--12018},\n  doi={10.1109/TIFS.2025.3626963}\n}\n"
dataset:
  id: SpeechAntiSpoofingBenchmarks/PyAra
  revision: 43f03384ee9ad701a64e0baaa531c8aedd724cd8
  split: test
scores:
  eer_percent: 4.013155212481144
  n_trials: 201778
  n_skipped: 0
artifact:
  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Nes2Net/resolve/e2c486f08d074d1dc74d350d8d1cdc4d5332cad6/.eval_results/SpeechAntiSpoofingBenchmarks/PyAra/scores.txt
  scores_sha256: 6cfd6bb6226241647bbb225d26b844ff1f6b7417836acd6717defc3cd6bc6860
  bench_version: speech-spoof-bench==0.3.4
reproduction:
  reproduced_by: SpeechAntiSpoofingBenchmarks
  reproduced_at: '2026-06-10'
  reproduced_bench_version: speech-spoof-bench==0.3.4
  match: scoring
submitter:
  hf_username: korallll
  contact: k.n.borodin@mtuci.ru
submitted_at: '2026-06-10'
notes: XLS-R 300M (wav2vec 2.0) front-end + Nes2Net-X back-end, the single (non-averaged) checkpoint from
  Liu-Tianchi/Nes2Net_ASVspoof_ITW (Nes_ratio [8,8], SE_ratio [1], pool_func 'mean', dilation 2). Architecture
  is built from the base xlsr2_300m.pt model config, then every weight is overwritten by the fine-tuned
  checkpoint. Deterministic first-64600-sample window (no random crop), matching the source data_utils_SSL.py::pad
  used at eval (default --test_protocol 4sec). score = output logit for class 1 (bona fide); higher =
  more bona fide. Back-end params ~0.51M; params_millions reports the full deployed model incl. the XLS-R
  front-end.